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Conditional Mean Model Estimation with Equality Constraints

For conditional mean model estimation, estimate requires
an arima model and a vector of univariate time series
data. The model specifies the parametric form of the conditional mean
model that estimate estimates. estimate returns
fitted values for any parameters in the input model with NaN values.
If you pass a T×r exogenous covariate matrix
in the X argument, then estimate returns r regression
estimates . If you specify non-NaN values for any
parameters, estimate views these values as equality
constraints and honors them during estimation.

For example, suppose you are estimating a model without a constant
term. Specify 'Constant',0 in the model you pass
into estimate. estimate views this
non-NaN value as an equality constraint, and does
not estimate the constant term. estimate also honors
all specified equality constraints while estimating parameters without
equality constraints. You can set a subset of regression coefficients
to a constant and estimate the rest. For example, suppose your model
is called model. If your model has three exogenous
covariates, and you want to estimate two of them and set the other
to one to 5, then specify model.Beta = [NaN 5 NaN].

estimate optionally returns the variance-covariance
matrix for estimated parameters. The parameter order in this matrix
is: